We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

https://cyberleninka.org/article/n/1407659.pdf

Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

The proposed method uses a local search technique which is embedded in particle swarm optimization (PSO) to select the reduced sized and salient feature subset. The goal of the local search technique is to guide the PSO search process to select distinct features by using their correlation information. Therefore, the proposed method selects the subset of features with reduced redundancy. A hybrid feature selection method based on particle swarm optimization is proposed.Our method uses a novel local search to enhance the search process near global optima.The method efficiently finds the discriminative features with reduced correlations.The size of final feature set is determined using a subset size detection scheme.Our method is compared with well-known and state-of-the-art feature selection methods. Feature selection has been widely used in data mining and machine learning tasks to make a model with a small number of features which improves the classifier's accuracy. In this paper, a novel hybrid feature selection algorithm based on particle swarm optimization is proposed. The proposed method called HPSO-LS uses a local search strategy which is embedded in the particle swarm optimization to select the less correlated and salient feature subset. The goal of the local search technique is to guide the search process of the particle swarm optimization to select distinct features by considering their correlation information. Moreover, the proposed method utilizes a subset size determination scheme to select a subset of features with reduced size. The performance of the proposed method has been evaluated on 13 benchmark classification problems and compared with five state-of-the-art feature selection methods. Moreover, HPSO-LS has been compared with four well-known filter-based methods including information gain, term variance, fisher score and mRMR and five well-known wrapper-based methods including genetic algorithm, particle swarm optimization, simulated annealing and ant colony optimization. The results demonstrated that the proposed method improves the classification accuracy compared with those of the filter based and wrapper-based feature selection methods. Furthermore, several performed statistical tests show that the proposed method's superiority over the other methods is statistically significant.

A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy

In this paper, optimal thresholds for multi-level thresholding in an image are obtained by maximizing the Tsallis entropy using cuckoo search algorithm. The method is considered as a constrained optimization problem. The solution is obtained through the convergence of a meta-heuristic search algorithm. The proposed algorithm is tested on standard set of images. The results are then compared with that of bacteria foraging optimization (BFO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO) and genetic algorithm (GA). Results are analyzed both qualitatively and quantitatively. It is observed that our results are also encouraging in terms of CPU time and objective function values.

Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm

Lectures on Nonextensive Statistical Mechanics.- I. Nonextensive Statistical Mechanics and Thermodynamics: Historical Background and Present Status.- II. Quantum Density Matrix Description of Nonextensive Systems.- III. Tsallis Theory, the Maximum Entropy Principle, and Evolution Equations.- IV. ComputationalMetho ds for the Simulation of Classical and Quantum Many Body Systems Arising from Nonextensive Thermostatistics.- Further Topics.- V. Correlation Induced by Nonextensivity and the Zeroth Law of Thermodynamics.- VI. Dynamic and Thermodynamic Stability of Nonextensive Systems.- VII. Generalized Simulated Annealing Algorithms Using Tsallis Statistics: Application to +-J Spin Glass Model.- VIII. Protein Folding Simulations by a Generalized-Ensemble Algorithm Based on Tsallis Statistics.

Nonextensive statistical mechanics and its applications

A novel adaptive cuckoo search algorithm, without using a Levy step, is proposed.The new algorithm improves the objective function values with a faster rate.Our evolutionary face recognition algorithm provides improved recognition rate.Optimal dimension reduction is achieved using PCA+IDA algorithm.ACS-IDA algorithm search optimal eigenvectors to improve accuracy. This paper presents a novel adaptive cuckoo search (ACS) algorithm for optimization. The step size is made adaptive from the knowledge of its fitness function value and its current position in the search space. The other important feature of the ACS algorithm is its speed, which is faster than the CS algorithm. Here, an attempt is made to make the cuckoo search (CS) algorithm parameter free, without a Levy step. The proposed algorithm is validated using twenty three standard benchmark test functions. The second part of the paper proposes an efficient face recognition algorithm using ACS, principal component analysis (PCA) and intrinsic discriminant analysis (IDA). The proposed algorithms are named as PCA+IDA and ACS-IDA. Interestingly, PCA+IDA offers us a perturbation free algorithm for dimension reduction while ACS+IDA is used to find the optimal feature vectors for classification of the face images based on the IDA. For the performance analysis, we use three standard face databases-YALE, ORL, and FERET. A comparison of the proposed method with the state-of-the-art methods reveals the effectiveness of our algorithm.

A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition

This paper presents a new adaptive Cuckoo search (ACS) algorithm based on the Cuckoo search (CS) for optimization. The main thrust is to decide the step size adaptively from its fitness value without using the Levy distribution. The other idea is to enhance the performance from the point of time and global minima. The performance of ACS against standard benchmark function show that the proposed algorithm converges to best solution with less time than Cuckoo search.

A new adaptive Cuckoo search algorithm

An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding

Our evolutionary face recognition algorithm provides improved recognition rate.A novel adaptive crossover bacterial foraging optimization algorithm is proposed.The new algorithm improves the objective function values.Optimal dimension reduction is achieved using ACBFO-Fisher algorithm.ACBFO-Fisher algorithm search optimal eigenvectors to improve accuracy. This paper presents a modified bacterial foraging optimization algorithm called adaptive crossover bacterial foraging optimization algorithm (ACBFOA), which incorporates adaptive chemotaxis and also inherits the crossover mechanism of genetic algorithm. First part of the research work aims at improvising evaluation of the optimal objective function values. The idea of using adaptive chemotaxis is to make it computationally efficient and crossover technique is to search nearby locations by offspring bacteria. Four different benchmark functions are considered for performance evaluation. The purpose of this research work is also to investigate a face recognition algorithm with improved recognition rate. In this connection, we propose a new algorithm called ACBFO-Fisher. The proposed ACBFOA is used for finding optimal principal components for dimension reduction in linear discriminant analysis (LDA) based face recognition. Three well-known face databases, FERET, YALE and UMIST, are considered for validation. A comparison with the results of earlier methods is presented to reveal the effectiveness of the proposed ACBFO-Fisher algorithm.

A novel adaptive crossover bacterial foraging optimization algorithm for linear discriminant analysis based face recognition

Recently, the slime mould algorithm (SMA) has become popular in function optimization, because it effectively uses exploration and exploitation to reach an optimal solution or near-optimal solution. However, the SMA uses two random search agents from the whole population to decide the future displacement and direction from the best search agents, which limits its exploitation and exploration. To solve this problem, we investigate an adaptive approach to decide whether opposition-based learning (OBL) will be used or not. Sometimes, the OBL is used to further increase the exploration. In addition, it maximizes the exploitation by replacing one random search agent with the best one in the position updating. The suggested technique is called an adaptive opposition slime mould algorithm (AOSMA). The qualitative and quantitative analysis of AOSMA is reported using 29 test functions that consisting of 23 classical test functions and 6 recently used composition functions from the IEEE CEC 2014 test suite. The results are compared with state-of-the-art optimization methods. Results presented in this paper show that AOSMA’s performance is better than other optimization algorithms. The AOSMA is evaluated using Wilcoxon’s rank-sum test. It also ranked one in Friedman’s mean rank test. The proposed AOSMA algorithm would be useful for function optimization to solve real-world engineering problems.

/pdf/adaptive-opposition-slime-mould-algorithm-3jix3blwvl.pdf

Manoj Kumar Naik

Papers

A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition

A new adaptive Cuckoo search algorithm

An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding

A novel adaptive crossover bacterial foraging optimization algorithm for linear discriminant analysis based face recognition

Adaptive Opposition Slime Mould Algorithm